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are management programs are seen as an effective approach to meet the challenge of increasing numbers of patients with complex care needs.1,2 Targeting these programs to patients with multiple chronic conditions and at high risk for cost-intensive care offers the greatest opportunities for improving quality of care and reducing healthcare costs.3 Because care management programs require allocation of restricted human and financial resources, the targeted population should be not only cost-intensive but also “care sensitive.” Care sensitivity implies 2 dimensions. First, patients have to be approachable (ie, willing and able to participate in intensified care programs). Second, their clinical needs have to be actionable (ie, care programs should be able to mitigate their needs). Therefore, prac-ticable tools are needed to screen patient populations for “high-risk” and care-sensitive individuals.4Since the 1980s, several statistical models commonly known as predictive models have been developed to predict future healthcare utilization and costs.5,6 These models are based on morbidity, prior healthcare utilization, and cost data, which can easily be obtained from health insurance claims data. Predictive models can be used to identify high-risk individuals who may benefit from care management programs.7 Further characterization of these individuals has revealed actionable healthcare needs.8 When used to identify patients likely to benefit from care management programs, predictive modeling (PM)– based selection is hypothesized as superior to selection by primary care physician (PCP).9 However, to our knowledge, the approaches have not been directly compared. This article extends the existing literature on patient selection for care management programs with results from a direct comparison of case selection by PM, selection by PCP, and a combination of both. A long-term patient–provider relationship enables PCPs to assess a patient’s clinical risk and care sensitivity. The results of this comparison may be useful in developing an approach to identify care-sensitive patients who are at high risk for future healthcare utilization and who are likely to benefit from care management programs.
Methods
In 2009, insurance claims data among a systematic sample of all beneficiaries of the
Gen-Identification of Patients Likely to Benefit From
Care Management Programs
tobias Freund, Md; Cornelia Mahler, MA, RN, Phd; Antje erler, Md, MPh; Jochen Gensichen, Md, MA, MPh; dominik ose, drPh, MPh;
Joachim szecsenyi, Md, Msc; and Frank Peters-Klimm, Md
Objectives: To compare predictive modeling (PM), selection by primary care physician (PCP), and a combination of both as approaches to prospec-tive patient identification for care management programs.
Study Design: Observational study.
Methods: A total of 6026 beneficiaries of a statu-tory health insurance program in Germany served as a sample for patient identification by PM and selection by PCP. The resulting mutually exclusive subpopulations were compared for care needs (eg, morbidity burden), healthcare utilization (previous all-cause hospitalizations and predicted costs), and prior participation in intensified care programs (as a proxy for amenability). Data sources were insurance claims data and a patient survey.
Results: Patients were selected for eligibility in a care management program by PM (n = 301), selection by PCP (n = 203), or a combination of both (n = 32). Compared with 5490 nonselected patients, all eligible patients had significantly higher morbidity burden and more previous hospitalizations. Compared with selection by PCP, PM identified patients at significantly higher risk for future healthcare utilization, with predicted annual healthcare costs of €8760 (95% confidence interval [CI], €8314-€9205) vs €4541 (95% CI, €4094-€4989) (P <.01). Compared with patients selected by PM, patients selected by PCP had significantly higher rates of prior participation in intensified care programs (80.8% vs 56.4%,
P <.01). Patients selected independently by both
approaches seemed to be at high risk for future healthcare utilization, with predicted annual healthcare costs of €8279 (95% CI, €7465-€9092), and 84.6% had prior participation in intensified care programs.
Conclusions: Identification of high-risk patients most likely to benefit from and participate in care management programs may be facilitated by a combination of PM and selection by PCP.
(Am J Manag Care. 2011;17(5):345-352)
For author information and disclosures, see end of text. In this article
Take-Away Points / p346
www.ajmc.com
eral Regional Health Fund (Allgemeine Ortskrankenkasse [AOK]) from 10 primary care practices in southwestern Ger-many were obtained for PM and further analysis. The 10 pri-mary care practices (6 single practices and 4 group practices with 2 PCPs) were recruited from rural areas (5 practices) and from urban areas (5 practices). In Germany, 85% of the popu-lation are insured by sickness funds. These statutory health in-surance programs based on a social security–based healthcare system are mainly funded by earmarked premiums. In 2009, the AOK insured 34% of all patients insured by a social secu-rity–based healthcare system in Germany.10 The AOK covers all areas of healthcare delivery, and its contributions are jointly paid by beneficiaries and by employers. There are no age re-strictions for AOK beneficiaries. Beneficiaries with their own income have copayments of up to 2% of their annual income.
The data set included medical and pharmacy claims from January 2007 to December 2008. Deidentified data were ob-tained from AOK beneficiaries of all ages. This explorative study was part of a series of studies to develop a care manage-ment program for high-risk patients in primary care. Details of the study have been described elsewhere.11 The University Hospital Heidelberg Institutional Review Board, Heidelberg, Germany, approved the study. Informed consent was obtained from all participants in the survey.
Case Finding by PM
For PM, we used the software package Case Smart Suite Ger-many (Verisk Health, Munich, GerGer-many). This PM program is an extension of diagnostic cost group PM, which has previously been applied in comparative case-finding studies.12 Information from the past 2 years (2007-2008) served as inputs for PM, in-cluding all International Statistical Classification of Diseases, 10th Revision (German modification) (ICD-10-GM) diagnosis codes assigned in outpatient and inpatient settings, prior costs, and hospital admissions, as well as demographic data. Clinically similar ICD-10-GM codes are classified into diagnostic groups. These groups are then further collapsed into condition catego-ries, which reflect similar levels of resource use and are orga-nized by body system or disease (eg, congestive heart failure). Individuals may have multiple diagnostic groups or condition
categories. In the next step, grouping into hierarchical clinical categories is applied to every individual. Therefore, each in-dividual is labeled exclusively, with the highest hierarchical clinical category within 1 condition category (eg, acute congestive heart failure exacerbation). However, different coexisting hierarchi-cal clinihierarchi-cal categories within 1 individual will increase predicted future healthcare utilization. Based on logistic regression analysis, the software package computes a likelihood of hospitalization for each in-dividual, which indicates the likelihood of at least 1 hospital admission within the next 12 months (2009-2010 for the pres-ent study).13 Despite the fact that hospitalizations are a source of major healthcare spending, predicted costs and predicted hospitalizations may not be used interchangeably to identify patients at high risk who may potentially benefit from care management interventions. Hospitalizations may reflect po-tentially actionable costs, as a significant proportion of these are owing to ambulatory care–sensitive conditions,14 whereas distinct cost-intensive procedures like fertility treatment, dialy-sis, biological agents, or chemotherapy seem to represent less actionable costs. For this study, patients with a likelihood of hospitalization score above the 90th percentile were defined as high-risk patients. We included patients with at least 1 of the following ICD-10-GM index conditions: type 2 diabetes mellitus (codes E11-E14), chronic obstructive pulmonary dis-ease (codes J43-J44), asthma (code J45), chronic heart failure (codes I11.0, I13.0, I13.2, I25.5, and I50), and late-life depres-sion (codes F32-F33 [>60 years]). Excluded were patients with dementia (codes F00-F03), dialysis (codes Z49 and Z99.2), or active cancer disease (codes C00-C97).
Case Finding Using Selection by PCP
Fourteen PCPs from 10 participating primary care practices were asked to screen a list of all AOK beneficiaries in their practice to select up to 30 patients for future participation in a care management program aimed at reducing avoidable hospi-talizations. Case selection was restricted to the same inclusion and exclusion criteria as aforedescribed. Primary care physi-cians were informed about the aims and intervention elements of the planned care management intervention. However, avoidable hospitalizations were not further specified before case selection. Primary care physicians were blinded to results of PM until they submitted their final list of selected patients.
Characterization of Selected Patients
We analyzed insurance claims data for all 6026 beneficia-ries to determine morbidity burden and prior healthcare
uti-Take-Away Points
Care management programs improve quality of care and reduce costs for patients at high risk for future healthcare utilization and actionable care needs. The best approach to identify patients most likely to benefit from care management programs remains unclear.
n Predictive modeling identifies patients who are at high risk for future healthcare utiliza-tion and who seem less approachable for care management.
n Selection by primary care physician identifies patients who are at lower risk for future healthcare utilization and who seem approachable for care management.
n When combined, the 2 approaches may complement each other to identify patients who are most likely to benefit from and participate in care management programs.
cally significant. All statistical analyses were performed using SPSS version 18.0 (SPSS Inc, Chicago, IL).
Results
The mean number of AOK beneficiaries per primary care practice was 603 (95% CI, 409-797). All beneficiaries were screened independently for case finding by PM and selection by PCP. Primary care physicians screened a mean of 464 (95% CI, 294-631) AOK beneficiaries per practice.
Predictive modeling identified 301 patients for eligibility in a care management program, while selection by PCP identified 203 patients who would seem to benefit from a care manage-ment intervention on the basis of clinical judgmanage-ment. Another group of 32 patients was concordantly identified using both PM and selection by PCP. All groups were mutually exclusive.
Eligible patients identified using PM or selection by PCP were significantly older (P <.01) than nonselected patients (Table 1). Compared with nonselected patients, eligible pa-tients had a significantly higher likelihood of future hospi-talization (P <.01) and a 2-fold (selection by PCP) to 4-fold (PM) higher morbidity burden as indicated by the Charlson Comorbidity Index (P <.01). Predicted annual healthcare costs for nonselected patients were €2882 (95% CI, € 2798-€2966), whereas significantly higher costs (P <.01) were predicted for patients selected by PCP (€4541 [95% CI, €4094-€4989]), PM (€8760 [95% CI, €8314-€9205]), or a combination of both (€8279 [95% CI, €7465-€9092]). Compared with patients selected by PM, patients selected by PCP had significantly higher rates of prior participation in in-tensified care programs (80.8% vs 56.4%, P <.01).
Compared with nonselected patients, PM identified pa-tients with significantly more prior hospitalizations (P <.01), whereas patients selected by PCPs showed no significant dif-ference in prior hospitalizations. Compared with patients selected by PM, the mutually exclusive subgroup of patients identified concordantly by PM and selection by PCP demon-strated no significant differences in morbidity burden, previ-ous hospitalizations, or predicted healthcare utilization.
The prevalences of 8 chronic conditions known to be clinically relevant and potentially sensitive to ambulatory care differed among patients identified using PM, selection by PCP, or a combination of both. In general, all 3 subpopu-lations showed higher prevalences of any of these conditions compared with nonselected patients. However, 1 of every 2 patients selected by PM had chronic heart failure, while selection by PCP resulted in a 3-fold lower prevalence of chronic heart failure. Patients identified concordantly by PM and selection by PCP had a prevalence of chronic heart fail-ure similar to that among patients identified by PM. lization. An adapted version of the Charlson Comorbidity
Index was used to determine weighted comorbidity counts based on inpatient and outpatient ICD-10-GM diagnoses.15 This index has been shown to be predictive of 1-year mor-tality and healthcare costs.16 Single index condition preva-lence, prior hospital admissions, and demographic variables were obtained from insurance claims data (2007-2008). We invited all selected patients (by PM, selection PCP, or a combination of both) who were enrolled in a PCP-centered care contract to fill out a paper-based questionnaire con-taining the following instruments: European Quality of Life 5D index17 to measure health-related quality of life, Medi-cation Adherence Report Scale18 to measure self-reported medication adherence, and additional items to measure so-ciodemographic variables. Because of data security regula-tions, patient participation in the survey was restricted to enrollees in a PCP-centered care contract.19 Participation in the patient survey was incentivized by a lottery of 5 shop-ping vouchers (€100).
In Germany, population-based disease management programs (DMPs) are part of routine care for 4 of the index conditions (type 2 diabetes mellitus, chronic obstructive pulmonary disease, asthma, and congestive heart failure caused by coronary heart disease).20 German DMPs con-sist of regular follow-up visits up to every 3 months. They include clinical examination, laboratory tests (eg, glyco-sylated hemoglobin tests), and patient education.21 How-ever, essential elements of care management interventions like individualized assessment, care planning, and frequent symptom monitoring are not routinely part of DMPs.22 Par-ticipation in German DMPs is voluntary and free of charge. German sickness funds are free to set incentives for patients to be enrolled in intensified care programs. The AOK de-cided to partly exempt beneficiaries from copayments (up to €40 a year) if they are willing to participate in DMPs. We considered prior voluntary participation in at least 1 of these programs during 2007-2008 as a proxy for “approach-ability,” indicating patients’ willingness to actively man-age their disease and to participate in a care manman-agement program.23
Data Analysis
Quantitative data are presented as means, 95% confi-dence intervals (CIs) of means, absolute numbers, and pro-portions. Two-sided c2 test was used to compare distributions of categorical variables. Means of continuous variables were compared by univariate analysis of variance, with perfor-mance of Games-Howell test as a statistical post hoc test.24 This test accounts for unequal variance and largely heteroge-neous sample sizes. P <.05 (2-sided) was considered
statisti-In total, 70.1% (376 of 536) of selected patients were en-rolled in a PCP-centered care contract and were eligible for the patient survey (Figure). Predictive modeling identified 58.5% (176 of 301) of enrollees, and selection by PCP identi-fied 84.7% (172 of 203) of enrollees. In total, 40.7% (153 of 376) of patients responded to the survey. Patients selected by PCP responded more frequently to the patient survey than patients selected by PM (58.1% vs 22.7%, P <.01). A total of 46.1% of patients identified concordantly by PM and selec-tion by PCP responded to the survey (Table 2).
Most survey respondents had a low level of education (eighth grade or less) (Table 2). More than one-third of se-lected patients lived alone. Their health-related quality of life was poor overall. Patients selected by PM had significantly lower European Quality of Life 5D index scores (P <.05) than
patients selected by PCP. One-third to one-half of selected patients reported problems with activities of daily living, and up to one-third of patients reported serious pain or bodily problems. Data from the Medication Adherence Report Scale showed that about one-half of selected patients might have had problems with medication adherence (summary score, <25). Prior participation in DMPs was significantly less fre-quent among patients selected by PM (P <.01) compared with patients selected by PCP.
Analysis of insurance claims data revealed that nonrespon-dents to the survey were similar in age to responnonrespon-dents (Table 3). However, nonrespondents had significantly more hospital admissions, greater morbidity burden, and higher predicted future healthcare costs and hospitalizations (P <.01 for all) compared with survey respondents.
n Table 1. Characteristics of Selected and Nonselected Patients Based on Insurance Claims Data Analysis P Characteristic (1) Patients Not Selected (n = 5490) (2) Patients Selected by PM (n = 301) (3) Patients Selected by PCP (n = 203) (4) Patients Selected by Both (n = 32) (1) vs (2) (1) vs (3) (2) vs (3) (2) vs (4) Age, mean (95% CI), y 53.5 (53.0-54.0) 74.7 (73.3-76.0) 66.3 (64.8-67.9) 72.7 (68.7-76.6) <.01 <.01 <.01 .77
Female sex, No. (%) 3188 (58.1) 179 (59.5) 108 (53.2) 17 (53.1) .48
Predicted likelihood of hospitalization, mean (95% CI) 0.189 (0.185-0.192) 0.594 (0.580-0.607) 0.253 (0.237-0.269) 0.556 (0.524-0.589) <.01 <.01 <.01 .77 Predicted annual healthcare costs, mean (95% CI), € 2882 (2798-2966) 8760 (8314-9205) 4541 (4094-4989) 8279 (7465-9092) <.01 <.01 <.01 .72 Hospital admissions in 2007-2008, mean (95% CI), No. 0.49 (0.46-0.51) 2.74 (2.51-2.97) 0.53 (0.39-0.66) 2.19 (1.66-2.72) <.01 .94 <.01 .23 Charlson Comorbidity
Index, mean (95% CI) 1.13 (1.09-1.18) 4.33 (4.00-4.65) 2.11 (1.88-2.34) 3.78 (2.98-4.58) <.01 <.01 <.01 .58 Comorbidity, No. (%) type 2 diabetes mellitus 1076 (19.6) 201 (66.8) 127 (62.6) 26 (81.3) <.01 Chronic obstructive pulmonary disease 482 (8.8) 93 (30.9) 28 (13.8) 14 (43.8) <.01 Congestive heart failure 381 (6.9) 151 (50.2) 30 (14.8) 14 (43.8) <.01 depression 1105 (20.1) 124 (41.2) 52 (25.6) 14 (43.8) <.01 Alcohol abuse 735 (13.4) 110 (36.5) 44 (21.7) 7 (21.9) <.01 hypertension 2497 (45.5) 280 (93.0) 162 (79.8) 31 (96.9) <.01 Ischemic heart disease 915 (16.7) 194 (64.5) 72 (35.5) 17 (53.1) <.01 osteoarthritis 1650 (30.1) 197 (65.4) 86 (42.4) 17 (53.1) <.001 Chronic conditions, mean (95% CI), No.
1.61 (1.57-1.65) 4.49 (4.33-4.64) 2.96 (2.78-3.14) 4.38 (4.02-4.73) <.01 <.01 <.01 .94
dIsCussIoN
The objective of this study was to compare 3 different ap-proaches for the identification of patients likely to benefit from care management programs. Compared with selection by PCP, diagnostic cost group–based PM for likelihood of hos-pitalization identified patients at greater risk for future health-care utilization. These patients had a higher morbidity burden and clinically relevant and potentially ambulatory care–sen-sitive conditions. Further characterization of these high-risk patients revealed substantial restrictions in quality of life and activities of daily living, indicating that they might benefit from complex care management programs. However, a large proportion of these patients may not be approachable for such
programs. Prior participation in population-based DMPs and response rates to the patient survey were significantly lower among the high-risk individuals selected by PM compared with those identified using selection by PCP or concordantly by PM and selection by PCP. This finding may be indicative of lower care sensitivity because of a lack of approachability. Remarkably, selection by PCP identified young patients with higher quality-of-life scores compared with those selected by PM. While chronic heart failure and depression are common target conditions for care management programs and were ex-plicitly chosen as inclusion criteria for this study, only small proportions of patients with these chronic conditions were identified by PCPs compared with PM. In contrast to PM, PCPs did not seem to use prior hospitalizations as an
indica-n Figure. Flow of Patients in the Study
PCP indicates primary care provider; PM, predictive modeling.
6026 Patients from 10 primary care
clinics 32 Patients selected independently by PM and PCP 536 Selected patients in total (eligible for insurance
claims data analysis)
376 Selected patients eligible for patient survey 153 Survey respondents 301 Patients selected by PM 203 Patients selected by PCP 160 Patients not enrolled in PCP-centered care contract
125 PM 31 PCP 4 Both 223 Nonrespondents 136 PM 72 PCP 15 Both 5490 Patients not selected for care management
tor of eligibility for care management programs. Although the actual motives of PCPs for patient selection remain unclear, a significant proportion of patients identified by PM may be too ill to benefit from care management programs.
The small subgroup identified concordantly by PM and se-lection by PCP had a high risk of future healthcare utilization and seemed likely to be approachable for intensified care pro-grams. Their risk profile was similar to that of patients selected by PM, but significantly more of the concordantly identified patients were willing to participate in intensified care programs.
Prior studies on clinical characteristics of high-risk indi-viduals reported similar results. Forrest et al7 compared differ-ent versions of PM software and suggested that clinically based PM may help identify patients who would benefit from care management programs as indicated by prior healthcare utiliza-tion and morbidity burden. Sylvia et al8 analyzed the top 18% of high-risk patients who were predicted by adjusted clinical group PM about future healthcare costs. In our study, clini-cal characteristics of respondents were comparable to those of respondents. Similar to our findings, Sylvia et al reported that nonrespondents to their survey were at highest risk for future healthcare utilization. Guided Care, a large care management trial that targeted patients above the 75th percentile of risk for future healthcare costs using the Centers for Medicare &
Medicaid Services hierarchical clinical categories model for PM, was able to recruit only about 38% of all eligible patients selected by PM.25 Because of data security restrictions in the present study, nonrespondents herein could not be further characterized about risk profile and care sensitivity markers.
Implications for Care Management Programs
As care management programs are increasingly integrated into routine care, practicable concepts for case finding will be required. Data from our study support the hypothesis that PM may be superior to selection by PCP in screening for high-risk cases. However, because patients at highest risk may not be amenable to active participation in care manage-ment programs, PCPs may be able to refine PM selection to target patients as both high risk and care sensitive. A com-bined approach may be more favorable than PM or selection by PCP alone.26 Further research should determine underly-ing variables for care sensitivity that could be included in insurance claims data–based PM. This may result in more precise case-finding algorithms with shorter lists of potential participants. Because screening requires human and financial resources, this approach may enable more efficient care man-agement programs that will reach more patients who are likely to benefit. Furthermore, standardized assessment tools should
n Table 2. Characteristics of Respondents to the Patient Survey
P Characteristic (a) Patients Selected by PM (n = 40) (b) Patients Selected by PCP (n = 100) (c) Patients Selected by Both (n = 13) (a) vs (b) (a) vs (c) (b) vs (c) Age, mean (95% CI), y 73.9 (70.6-77.1) 68.8 (66.8-70.8) 76.9 (71.8-82.1) .02 .98 .02
Female sex, No. (%) 23 (57.5) 48 (49.5) [97] 9 (69.2) .34
<Eighth grade education, No. (%) 35 (89.7) [39] 80 (85.1) [94] 12 (92.3) .64 Living alone, No. (%) 18 (46.2) [39] 27 (31.4) [86] 4 (33.3) [12] .28 European Quality of Life 5D index
score, mean (95% CI) 0.57 (0.48-0.66) 0.70 (0.66-0.75) 0.55 (0.38-0.72) .02 >.99 .13
Activities of daily living, No. (%) (n = 38) (n = 95) (n = 13) .03
No problems 16 (42.1) 67 (70.5) 7 (53.8)
some problems 19 (50.0) 25 (26.3) 6 (46.2)
serious problems 3 (7.9) 3 (3.2) 0
Pain or bodily complaints, No. (%) (n = 39) (n = 96) (n = 12) .29
No problems 6 (15.4) 18 (18.8) 1 (8.3)
some problems 23 (59.0) 65 (67.7) 7 (58.3)
serious problems 10 (25.6) 13 (13.5) 4 (33.3)
Nonadherence, No. (%) 16 (45.7) [35] 36 (40.4) [36] 5 (45.5) [11] .63 Prior participation in disease
management program, No. (%)
22 (56.4) [39] 80 (80.8) [99] 11 (84.6) <.01
be developed and implemented to assist clinicians in refin-ing PM-based selection. However, care management programs may be unable to reach a large proportion of patients who are at highest risk. This unsolved problem requires further inves-tigation as to how these patients might be targeted by intensi-fied care programs, if possible at all.
Limitations
The results of this study should be interpreted in light of several limitations. Although we performed an explorative study, our results may be of relevance as the first empirical comparison to date of different target populations for care management programs identified using PM and selection by PCPs. We included beneficiaries of a large German sickness fund who lived in a single geographic area and were patients at small-to-medium primary care practices. Interpretation of our results should consider potential differences among patient populations of other sickness funds, regions, and na-tions. The observational study design does not allow us to draw final conclusions about the most effective approach to identify patients who benefit from care management pro-grams. This would require clinical trials comparing the effects of different case-finding approaches on patient outcomes and costs. However, our results may facilitate further research in this field. In addition, this study did not compare complex PM with simpler prediction tools like prior costs alone, which seem to be inferior in targeting patients to reduce hospitaliza-tion rates.7
Case selection by PCPs may be made on an individ-ual basis. We did not assess motives and criteria that guide PCP-based patient selection for care management programs. Qualitative research may close this knowledge gap and con-tribute to a comprehensive understanding of care sensitivity, which has been only roughly operationalized in this study. We used prior participation in DMPs as a proxy for the care sen-sitivity domain of approachability. While this could be useful in the sense that patients who are unwilling to participate in less complex German DMPs would hardly be willing to
par-ticipate in care management programs, it cannot automati-cally be seen as a valid predictor of actual participation in care management programs. Other indicators for care sensitivity may be revealed through further research in the field.
Insurance claims data analyses have specific limitations. Diagnostic coding may be strongly cost driven through remu-neration procedures.27 Therefore, insurance claims data–based analyses of morbidity burden may need careful interpreta-tion.28 However, the combination of outpatient and inpatient diagnostic codes used in the present study may have reduced the coding biases.
Interpretation of our survey results should take into ac-count the small sample sizes and high rates of nonresponse. Differing response rates of patients selected by PM and PCPs may have been influenced by selection bias. Primary care pro-viders might have been more motivated to include patients whom they had proposed for participation in care manage-ment programs. However, because potential participants in these programs may be approached by their PCPs, this finding may be significant for program providers.
Finally, beneficiaries not enrolled in a PCP-centered care contract were excluded from the patient survey because of data security regulations. This may further limit interpreta-tion of our results. However, enrollees in PCP-centered care contracts are known to be high-risk patients having a higher morbidity burden compared with beneficiaries who are not enrolled in these contracts.29 This may have led to an under-estimation of the care needs among the selected patient popu-lation in our study.
Author Affiliations: From the Department of General Practice and Health
Services Research (TF, CM, DO, JS, FPK), University Hospital Heidelberg, Heidelberg, Germany; Institute of General Practice (AE), Frankfurt am Main, Germany; and Institute of General Practice (JG), Friedrich Schiller University Jena, Jena, Germany.
Funding Source: This study was supported by the General Regional
Health Fund (Allgemeine Ortskrankenkasse), Baden-Württemberg, Germany.
Author Disclosures: The authors (TF, CM, AE, JG, DO, JS, FPK) report
no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.
n Table 3. Characteristics of Respondents and Nonrespondents to the Patient Survey Mean (95% CI) Characteristic Respondents (n = 153) Nonrespondents (n = 223) P Age, y 71.1 (69.5-72.8) 71.0 (69.3-72.6) .90
Predicted likelihood of hospitalization 0.373 (0.344-0.402) 0.474 (0.449-0.500) <.01
Predicted annual healthcare costs, € 5572 (5088-6056) 7616 (7011-8220) <.01
Charlson Comorbidity Index 2.80 (2.45-3.15) 3.43 (3.09-3.78) .01
hospital admissions in 2007-2008, No. 1.20 (0.94-1.46) 1.80 (1.58-2.03) <.01
Authorship Information: Concept and design (TF, CM, AE, JG, JS, FPK);
acquisition of data (TF); analysis and interpretation of data (TF, CM, JG, DO, FPK); drafting of the manuscript (TF, JG, FPK); critical revision of the manuscript for important intellectual content (CM, AE, JG, DO, JS, FPK); statistical analysis (TF, AE, DO); provision of study materials or patients (AE); obtaining funding (AE, JS); administrative, technical, or logistic sup-port (AE); and supervision (AE, JS, FPK).
Address correspondence to: Tobias Freund, MD, Department of
Gen-eral Practice and Health Services Research, University Hospital Heidelberg, Vobstrasse 2, D-69115 Heidelberg, Germany. E-mail: tobias.freund@med. uni-heidelberg.de.
ReFeReNCes
1. Bodenheimer T, Berry-Millet R. Care Management of Patients With
Complex Health Care Needs. Princeton, NJ: Robert Wood Johnson
Foundation; December 2009. Research synthesis report 19.
2. Starfield B, Lemke KW, Bernhardt T, Foldes SS, Forrest CB, Weiner JP.
Comorbidity: implications for the importance of primary care in ‘case’ management. Ann Fam Med. 2003;1(1):8-14.
3. Goetzel RZ, Ozminkowski RJ, Villagra VG, Duffy J. Return on
invest-ment in disease manageinvest-ment: a review. Health Care Financ Rev. 2005; 26(4):1-19.
4. Bernstein RH. New arrows in the quiver for targeting care
manage-ment: high-risk versus high-opportunity case identification. J Ambul
Care Manage. 2007;30(1):39-51.
5. Ash AS, Ellis RP, Pope GC, et al. Using diagnoses to describe
popula-tions and predict costs. Health Care Financ Rev. 2000;21(3):7-28.
6. Johns Hopkins ACG System. ACG predictive model. http://www.acg.
jhsph.edu. Accessed September 24, 2010.
7. Forrest CB, Lemke KW, Bodycombe DP, Weiner JP. Medication,
diag-nostic, and cost information as predictors of high-risk patients in need of care management. Am J Manag Care. 2009;15(1):41-48.
8. Sylvia ML, Shadmi E, Hsiao CJ, Boyd CM, Schuster AB, Boult C.
Clinical features of high-risk older persons identified by predictive modeling. Dis Manag. 2006;9(1):56-62.
9. Boyd CM, Boult C, Shadmi E, et al. Guided care for multimorbid
older adults. Gerontologist. 2007;47(5):697-704.
10. German Ministry of Health. http://www.bundesgesundheits
ministerium.de/cln_178/nn_1168278/DE/Ministerium/Statistik/ Statistik.html. Accessed December 15, 2010.
11. Freund T, Wensing M, Mahler C, et al. Development of a primary
care–based complex care management intervention for chronically ill patients at high risk for hospitalization: a study protocol. Implement
Sci. 2010;5:e70.
12. Weir S, Aweh G, Clark RE. Case selection for a Medicaid chronic
care management program. Health Care Financ Rev. 2008;30(1):61-74.
13. Verisk Health. Likelihood of Hospitalization (LOH): Kaiser study.
http://www.veriskhealth.com/content/verisk-healthcare-white-papers-and-research. Accessed September 28, 2010.
14. Purdy S, Griffin T, Salisbury C, Sharp D. Ambulatory care sensitive
conditions: terminology and disease coding need to be more specific to aid policy makers and clinicians [published correction appears in
Public Health. 2010;124(12):720]. Public Health. 2009;123(2):169-173.
15. Sundararajan V, Henderson T, Perry C, Muggivan A, Quan H, Ghali WA. New ICD-10 version of the Charlson Comorbidity Index predicted
in-hospital mortality. J Clin Epidemiol. 2004;57(12):1288-1294.
16. Perkins AJ, Kroenke K, Unützer J, et al. Common comorbidity
scales were similar in their ability to predict health care costs and mortality. J Clin Epidemiol. 2004;57(10):1040-1048.
17. Greiner W, Weijnen T, Nieuwenhuizen M, et al. A single European
currency for EQ-5D health states: results from a six-country study. Eur
J Health Econ. 2003;4(3):222-231.
18. Mahler C, Hermann K, Horne R, et al. Assessing reported
adher-ence to pharmacological treatment recommendations: translation and evaluation of the Medication Adherence Report Scale (MARS) in Germany. J Eval Clin Pract. 2010;16(3):574-579.
19. Klingenberg A, Broge B, Herholz H, Szecsenyi J, Ose D. Evaluation
of special GP-centered health care contracts in Germany by the par-ticipating GPs: present situation and future perspectives [in German].
Med Klin (Munich). 2010;105(2):89-95.
20. Busse R. Disease management programs in Germany’s statutory
health insurance system. Health Aff (Millwood). 2004;23(3):56-67.
21. Szecsenyi J, Rosemann T, Joos S, Peters-Klimm F, Miksch A.
German diabetes disease management programs are appropriate for restructuring care according to the chronic care model: an evaluation with the Patient Assessment of Chronic Illness Care instrument.
Diabetes Care. 2008;31(6):1150-1154.
22. Freund T, Kayling F, Miksch A, Szecsenyi J, Wensing M.
Effective-ness and efficiency of primary care based case management for chronic diseases: rationale and design of a systematic review and meta-analysis of randomized and non-randomized trials [CRD32009100316]. BMC Health Serv Res. 2010;10:e112.
23. Dunkelberg S, Zingel D, Noack A, van den Bussche H, Kadusz-kiewicz H. Which patients are (not) included in the DMP diabetes
programme [in German]? Gesundheitswesen. 2006;68(5):289-293.
24. Games PA, Howell JF. Pairwise multiple comparison procedures
with unequal n’s and/or variances: a Monte Carlo study. J Educ Stat. 1976;1(2):113-125.
25. Boyd CM, Reider L, Frey K, et al. The effects of Guided Care on the
perceived quality of health care for multi-morbid older persons: 18-month outcomes from a cluster-randomized controlled trial. J Gen
Intern Med. 2010;25(3):235-242.
26. Boult C, Pualwan TF, Fox PD, Pacala JT; HMO Workgroup on Care Management. Identification and assessment of high-risk seniors.
Am J Manag Care. 1998;4(8):1137-1146.
27. Retchin SM, Ballard DJ. Commentary: establishing standards for
the utility of administrative claims data. Health Serv Res. 1998;32(6): 861-866.
28. Erler A, Beyer M, Muth C, Gerlach FM, Brennecke R. Garbage in:
garbage out? validity of coded diagnoses from GP claims records [in German]. Gesundheitswesen. 2009;71(12):823-831.
29. Freund T, Szecsenyi J, Ose D. Characteristics of beneficiaries of a
GP-centred health care contract in Germany [in German]. Med Klin
